# pr#agma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # isort: skip_file # --- Do not remove these libs --- import numpy as np # noqa import pandas as pd # noqa from freqtrade.strategy.parameters import DecimalParameter, BooleanParameter, IntParameter from pandas import DataFrame import math from functools import reduce from freqtrade.strategy.interface import IStrategy # -------------------------------- # Add your lib to import here import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib # This class is a sample. Feel free to customize it. class StrategyPierrick4116(IStrategy): # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 2 # valeur de bbwidth pour démarrer buy_bollinger = DecimalParameter(0.025, 0.125, decimals=2, default=0.095, space="buy") buy_candel_bb1 = IntParameter(0, 10, default=5, space="buy") # Valeur de la deuxième condition bollinger avec condition sma200 buy_bollinger_2 = DecimalParameter(0.0, 0.08, decimals=2, default=0.04, space="buy") # volume à atteindre buy_volume = IntParameter(0, 50, default=0, space="buy") buy_candel_percent = DecimalParameter(1.00, 1.10, decimals=2, default=1.04, space="buy") sell_candel_percent = DecimalParameter(1.0, 1.10, decimals=2, default=1.04, space="sell") # buy_rsi = IntParameter(20, 40, default=30, space="buy") # buy_adx_enabled = BooleanParameter(default=True, space="buy") # buy_rsi_enabled = CategoricalParameter([True, False], default=False, space="buy") # buy_trigger = CategoricalParameter(["bb_lower", "macd_cross_signal"], default="bb_lower", space="buy") # ROI table: minimal_roi = { # "0": 0.015 "0": 0.5 } # Stoploss: stoploss = -1 trailing_stop = True trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.0175 # 0.015 trailing_only_offset_is_reached = True # max_open_trades = 3 # Optimal ticker interval for the strategy. timeframe = '5m' # Run "populate_indicators()" only for new candle. process_only_new_candles = False # These values can be overridden in the "ask_strategy" section in the config. use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 30 # Optional order type mapping. order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } # Optional order time in force. order_time_in_force = { 'buy': 'gtc', 'sell': 'gtc' } plot_config = { # Main plot indicators (Moving averages, ...) 'main_plot': { 'bb_lowerband': {'color': 'white'}, 'bb_upperband': {'color': 'white'}, 'sma100': {'color': 'green'}, 'sma10': {'color': 'yellow'}, 'sma20': {'color': 'cyan'} # 'rsi': {'color': '#c58893'} }, 'subplots': { # Subplots - each dict defines one additional plot "BB": { 'bb_width': {'color': 'white'}, }, "ADX": { 'adx': {'color': 'white'}, 'minus_dm': {'color': 'blue'}, 'plus_dm': {'color': 'red'} }, "Pct": { 'percent': {'color': 'white'} } } } def informative_pairs(self): return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # MACD # macd = ta.MACD(dataframe) # dataframe['macd'] = macd['macd'] # dataframe['macdsignal'] = macd['macdsignal'] # dataframe['macdhist'] = macd['macdhist'] # # Plus Directional Indicator / Movement dataframe['plus_dm'] = ta.PLUS_DM(dataframe) dataframe['plus_di'] = ta.PLUS_DI(dataframe) # Minus Directional Indicator / Movement dataframe['adx'] = ta.ADX(dataframe) dataframe['minus_dm'] = ta.MINUS_DM(dataframe) dataframe['minus_di'] = ta.MINUS_DI(dataframe) dataframe['min'] = ta.MIN(dataframe) dataframe['max'] = ta.MAX(dataframe) # # Aroon, Aroon Oscillator # aroon = ta.AROON(dataframe) # dataframe['aroonup'] = aroon['aroonup'] # dataframe['aroondown'] = aroon['aroondown'] # dataframe['aroonosc'] = ta.AROONOSC(dataframe) # RSI dataframe['rsi'] = ta.RSI(dataframe) # # EMA - Exponential Moving Average # dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3) # dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) # dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) # dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21) # dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) # # SMA - Simple Moving Average # dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3) # dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5) dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10) dataframe['sma20'] = ta.SMA(dataframe, timeperiod=20) dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50) dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100) dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200) # dataframe['sma200_95'] = ta.SMA(dataframe, timeperiod=200) * 0.95 # dataframe['sma200_98'] = ta.SMA(dataframe, timeperiod=200) * 0.98 # dataframe['sma500'] = ta.SMA(dataframe, timeperiod=500) # dataframe['sma500_90'] = ta.SMA(dataframe, timeperiod=500) * 0.9 # dataframe['sma500_95'] = ta.SMA(dataframe, timeperiod=500) * 0.95 # dataframe['sma500_20'] = ta.SMA(dataframe, timeperiod=500) * 0.2 dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"] # Bollinger Bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe["bb_percent"] = ( (dataframe["close"] - dataframe["bb_lowerband"]) / (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) ) dataframe["bb_width"] = ( (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"] ) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: value = 0 p = dataframe['close'].shift(20) / dataframe['close'] for k, v in p.iteritems(): # print(k, v) value = v # condition1 = np.where(value >= 1.04, True, False) conditions_bb_1 = [] # GUARDS AND TRENDS conditions_bb_1.append(dataframe['bb_width'] >= self.buy_bollinger.value) conditions_bb_1.append(dataframe['volume'] >= self.buy_volume.value * 1000) conditions_bb_1.append(dataframe['close'] < dataframe['bb_lowerband']) conditions_bb_1.append( (dataframe['open'] - dataframe['bb_lowerband']) / (dataframe['bb_lowerband'] - dataframe['close']) > self.buy_candel_bb1.value ) conditions_bb_1.append(dataframe['percent'] > -0.03) condition1 = reduce(lambda x, y: x & y, conditions_bb_1) conditions2_bb_2 = [] conditions2_bb_2.append(dataframe['bb_width'] <= self.buy_bollinger_2.value) conditions2_bb_2.append(dataframe['close'] > dataframe['open'] * self.buy_candel_percent.value) conditions2_bb_2.append(dataframe['close'] < dataframe['bb_upperband']) condition2 = reduce(lambda x, y: x & y, conditions2_bb_2) dataframe.loc[ ( condition1 ) | ( False & (dataframe['bb_width'] > 0.065) & (dataframe['close'] > dataframe['bb_upperband']) & (dataframe['close'].shift(1) < dataframe['bb_upperband'].shift(1)) & (dataframe['close'].shift(1) > dataframe['open'].shift(1)) & (dataframe['close'].shift(2) > dataframe['open'].shift(2)) & (dataframe['close'].shift(3) > dataframe['open'].shift(3)) & (dataframe['close'] < dataframe['open'].shift(3) * 1.03) & (dataframe['sma10'].shift(1) < dataframe['sma10'] * 1.01) & (dataframe['sma10'].shift(1) > dataframe['sma10'] * 0.99) ) | ( condition2 & # (dataframe['close'] > dataframe['open'] * 1.04) & # (dataframe['close'] <= dataframe['open'] * 1.05) & (dataframe['close'] > dataframe['sma10']) & (dataframe['open'] < dataframe['sma10']) & (dataframe['sma100'].shift(4) < dataframe['sma100'] * 1.01) & (dataframe['sma100'].shift(4) > dataframe['sma100'] * 0.99) # (dataframe['sma100'].shift(1) <= dataframe['sma100']) ) | ( False & (dataframe['close'] > dataframe['bb_upperband']) & (dataframe['close'] > dataframe['open'] * 1.02) & (dataframe['close'] > dataframe['sma100']) & (dataframe['open'] < dataframe['sma100']) & (dataframe['close'].shift(1) > dataframe['open'].shift(1)) & (dataframe['close'].shift(2) > dataframe['open'].shift(2)) ) , 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions_sell = [] # GUARDS AND TRENDS conditions_sell.append(dataframe['close'] < dataframe['open']) conditions_sell.append(dataframe['close'] < dataframe['bb_lowerband']) conditions_sell.append(dataframe['close'].shift(1) < dataframe['open'].shift(1)) conditions_sell.append(dataframe['close'].shift(2) < dataframe['open'].shift(2)) conditions_sell.append(dataframe['close'] * self.sell_candel_percent.value < dataframe['open'].shift(2)) condition1 = reduce(lambda x, y: x & y, conditions_sell) dataframe.loc[condition1, 'sell'] = 1 return dataframe